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Field
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based models to predict spatial variations in sediment carbon retention and blue carbon accretion capacity across coastal habitats. • Integrate hydrodynamic modelling outputs with sediment, restoration
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conduct research related to creating or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Basic Qualifications Core
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computational analysis of functional genomics datasets with an emphasis on single-cell omics. This person will develop new computational methods to analyze novel RNAseq, ATACseq, and Spatial Transcriptomics
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studies using primary human and mouse islets and pancreas tissue, cell-culture based systems, genetic mouse models, and disease models, with molecular biology, single-cell, spatial, imaging, and epigenomic
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of simulation-ready models. Experience with FEM/FDTD spatial discretization, geometry or voxel-model preparation, material-property assignment, and mesh/grid-quality assessment. Experience with Sim4Life, COMSOL
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Engineering at Harvard University seeks outstanding postdoctoral applicants with expertise in non-human primate immunology and virology or analysis of spatial omics data to join an interdisciplinary team
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tissue culture, animal models, high-parameter flow cytometry, spatial transcriptomics or proteomics, and cell gene engineering techniques is desired, but not required. This is an outstanding opportunity
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metabolomics, specifically tissue and single-cell-based spatial metabolomics using advanced mass spectrometry techniques. This is a unique opportunity to contribute to an ambitious, multifaceted research
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RNA sequencing and spatial biology (transcriptomics) datasets (desirable) Fluency in relevant models, techniques or methods and ability to contribute to developing new ones Ability to assess resource
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Stephen & Denise Adams Center for Parkinson's Disease at Yale School of Medicine | New Haven, Connecticut | United States | about 2 months ago
& Denise Adams Center for Parkinson’s Disease Research and Scherzer Neurogenomics Lab combine population-scale human data (>30,000 deeply phenotyped participants), single-cell and spatial omics, and next